%0 Journal Article
%T Enhancing BERTopic with Pre-Clustered Knowledge: Reducing Feature Sparsity in Short Text Topic Modeling
%A Qian Wang
%A Biao Ma
%J Journal of Data Analysis and Information Processing
%P 597-611
%@ 2327-7203
%D 2024
%I Scientific Research Publishing
%R 10.4236/jdaip.2024.124032
%X Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic capture accuracy. We propose a novel approach that incorporates pre-clustered knowledge into the BERTopic model while reducing the l2 norm for low-frequency words. Our method effectively mitigates feature sparsity during cluster mapping. Empirical evaluation on the StackOverflow dataset demonstrates that our approach outperforms baseline models, achieving superior Macro-F1 scores. These results validate the effectiveness of our proposed feature sparsity reduction technique for short-text topic modeling.
%K Topic Model
%K BERTopic
%K Short Text
%K Feature Sparsity
%K Cluster
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=137513